Select Pandas rows based on list index

Question

I have a dataframe df :

   20060930  10.103       NaN     10.103   7.981
   20061231  15.915       NaN     15.915  12.686
   20070331   3.196       NaN               


        

Answer 1

For large datasets, it is memory efficient to read only selected rows via the skiprows parameter.

Example

pred = lambda x: x not in [1, 3]
pd.read_csv("data.csv", skiprows=pred, index_col=0, names=...)

This will now return a DataFrame from a file that skips all rows except 1 and 3.

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Details

From the docs:

skiprows : list-like or integer or callable, default None

...

If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be lambda x: x in [0, 2]

This feature works in version pandas 0.20.0+. See also the corresponding issue and a related post.